ABSTRACT: Quiet submarine threats and high clutter in the littoral environment increase computation and communication demands on beamforming arrays, particularly for applications that require in-array autonomous operation. By coupling each transducer node in a distributed array with a microprocessor, and networking them together, embedded parallel processing for adaptive beamformers can glean advantages in execution speed, fault tolerance, scalability, power, and cost. In this thesis, a new parallel algorithm for Minimum Variance Distortionless Response (MVDR) adaptive beamforming on distributed systems is introduced for in-array sonar signal processing. Performance results are also included, among them execution times, parallel efficiencies, and memory requirements, using a distributed system testbed comprised of a cluster of workstations connected by a high-speed network.

Thesis:

Thesis (M.S.)--University of Florida, 1999.

Bibliography:

Includes bibliographical references (p. 99-100).

System Details:

System requirements: World Wide Web browser and Adobe Acrobat Reader to view and print PDF files.

iterations to successive nodes in the array, and both staggers and overlaps execution of

each iteration within and across nodes by pipelining. The communication component of

the algorithm reduces the communication latency of all-to-all communication with small

amounts of data by spooling multiple data samples into a single packet. In so doing, a

separate thread is employed to hide communication latency by performing the all-to-all

communication while the main computational thread is processing the data received

during the previous communication cycle. The next two sections discuss the two

algorithmic components in detail, followed by performance results in Chapter 6.

5.1 Computation component of DP-MVDR

As reviewed in Chapter 4, each beamforming iteration of the sequential MVDR

algorithm is composed of an ensemble averaging task, a matrix inversion task, and a

steering task. Before the task of matrix inversion can begin, the ensemble average of the

CSDMs must be computed. The steering task then uses the inverse of the CSDM average

to steer for every angle of interest. In the DP-MVDR algorithm, each node in succession

is scheduled to beamform a different data set in a round-robin fashion. For instance,

node 0 would be assigned to process the first set of data samples collected by the array,

node 1 the second set, and so forth, causing each node to beamform using every nth data

set, where n is the number of nodes.

Furthermore, the DP-MVDR algorithm also pipelines the matrix inversion and

steering tasks within each beamforming iteration by decomposing them into n stages

each. When a node is scheduled a new beamforming iteration, it computes the ensemble

average of the CSDM and executes the first stage of the matrix inversion task. The next

n-1 stages are spent completing the computation of the matrix inversion and the

following n stages perform the steering, thereby imposing a result latency of 2n pipeline

stages. Since a node is scheduled a new beamforming iteration every n stages, iterations

are overlapped within a node by computing a matrix inversion stage of the current

iteration and the respective steering stage of the previously scheduled iteration in the

same pipeline stage. Meanwhile, the running sum of CSDMs is updated at the beginning

of every pipeline stage. The pipeline structure of the computation component of the DP-

MVDR is shown in Figure 5.1 for n = 3.

Result latency
i Effective
Node 0 Iteration time

Node 1

Node 2

A different polygon for
each it ration
Legend Task being Stage number
U: Updating the running sum of CSDMs performed of current task
A: Averaging the CSDM / .0 0
I: Inversion of the CSDM A S--i!
S: Steering using previously inverted CSDM
*: beamform iteration completed Number of the Iteration
current task is working on